Earticle

The rise of wireless devices makes interference a key challenge for reliable communication in dense spectrum-sharing networks. This paper proposes a graph neural network (GNN)-based power control algorithm to minimize the worst-case outage probability by using statistical channel state information (CSI), i.e., position information. By representing the network as a fully connected directed graph with node and edge features derived from transceiver positions, the GNN employs message-passing layers to aggregate interference patterns and infer near-optimal transmit powers. Simulation results demonstrate the scalability and generalization capability of the proposed method, confirming its suitability for real-time deployment in large-scale wireless systems.

To solve the spectrum scarcity problem, dynamic spectrum access (DSA) technology has emerged as a promising solution. Effectively implementing DSA demands accurate and efficient spectrum prediction. However, complex spatiotemporal correlation and heterogeneity in spectrum observations usually make spectral prediction arduous and even ambiguous. In this letter, we propose a spectrum prediction method based on an attention-aided graph convolutional neural network (AttGCN) to capture features in both spatial and temporal dimensions. By leveraging the attention mechanism, the AttGCN adapts its attention weights at different time steps and spatial positions, thus enabling itself to seize changes in spatiotemporal correlations dynamically. Simulation results show that the proposed spectrum prediction method performs better than baseline algorithms in long-term forecasting tasks.

최근 활발히 연구되는 딥러닝 방법론은 인공지능의 성능을 급속도로 향상시켰고, 이에 따라 다양한 산업 분야에서 딥러닝을 활용한 시스템이 제시되고 있다. 교통 시스템에서는 GNN을 활용한 공간-시간 그래프 모델링이 교통 속도 예측에 효과적인 것으로 밝혀졌지만, 이는 메모 리 병목 현상을 유발하기 때문에 모델이 비효율적으로 학습된다는 단점이 있다. 따라서 본 연 구에서는 그래프 분할 방법을 통해 도로 네트워크를 분할하여 메모리 병목 현상을 완화함과 동시에 우수한 성능을 달성하고자 한다. 제안 방법론을 검증하기 위해 인천시 UTIC 데이터 분석 결과를 바탕으로 Jensen-Shannon divergence를 사용하여 도로 속도 분포의 유사도를 측정 하였다. 그리고 측정된 유사도를 바탕으로 스펙트럴 클러스터링을 수행하여 도로 네트워크를 군집화하였다. 성능 측정 결과, 도로 네트워크가 7개의 네트워크로 분할되었을 때 MAE 기준 5.52km/h의 오차로 비교 모델 대비 가장 우수한 정확도를 보임과 동시에 메모리 병목 현상 또 한 완화되는 것을 확인할 수 있었다.

Deep learning methodology, which has been actively studied in recent years, has improved the performance of artificial intelligence. Accordingly, systems utilizing deep learning have been proposed in various industries. In traffic systems, spatio-temporal graph modeling using GNN was found to be effective in predicting traffic speed. Still, it has a disadvantage that the model is trained inefficiently due to the memory bottleneck. Therefore, in this study, the road network is clustered through the graph clustering algorithm to reduce memory bottlenecks and simultaneously achieve superior performance. In order to verify the proposed method, the similarity of road speed distribution was measured using Jensen-Shannon divergence based on the analysis result of Incheon UTIC data. Then, the road network was clustered by spectrum clustering based on the measured similarity. As a result of the experiments, it was found that when the road network was divided into seven networks, the memory bottleneck was alleviated while recording the best performance compared to the baselines with MAE of 5.52km/h.

As urban networks integrate heterogeneous radio access technologies (RATs), such as Wi-Fi and 5G/B5G, modeling performance becomes challenging due to interference, spatial variability, and propagation conditions. This paper proposes a graph neural network (GNN)-based framework for predicting throughput, delay, and jitter in multi-RAT environments, considering RAT type. The model encodes network topology and channel characteristics using node and edge features, capturing spatial configuration, congestion, and line-of-sight (LoS) versus non-line-of-sight (NLoS) conditions. The results show that GNNs exhibit robustness across station densities and spatial conditions. The message-passing GNN method performs well for throughput and delay, while non-graph methods better estimate jitter.

Late blight, caused by Phytophthora infestans, threatens tomato and potato crops in Nepal. This study explores developing and deploying a mobile application powered by a graph neural network (GNN) to predict late blight risk for Nepali farmers. The GNN trained on 43 years of NASA satellite weather data can generate 5-days forecast for 320 weather stations in Sudurpashim and Karnali Province, Nepal. The mobile application offers user-friendly forecasts and visualizes late blight risk through clear graphical interfaces. In the visited sites, 30% of tomato and potato crops were found infected with late blight, which the app had identified as high-risk. Samples infected with late blight were collected and analyzed in a wet lab setting. All infected samples tested positive for P. infestans, confirming the app's ability to predict real-world late blight outbreaks. This study showcases the successful development and deployment of a GNN-powered mobile application for assessing late blight risk in Nepal. The application disseminates critical weather information and localized risk assessments, potentially enhancing late blight management in tomato and potato crops. Further research, including extensive field trials comparing with farmers' practices, could increase the application's usability in Nepali fields.

Our research aims to enhance the modeling of speech signals for more effective extraction of node features and analysis of relationships between nodes. To achieve this, we model speech signals as cyclic or linear graphs. Our model combines layers of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to leverage their respective strengths in processing graph data. Specifically, we utilize GCN to aggregate information from neighboring nodes, which helps capture local relationships among nodes. Additionally, we employ GAT mechanisms to assign varying attention weights to different neighboring nodes, facilitating a better capture of complex global relationships between nodes. In our experiments, we validate our approach using the IEMOCAP dataset and demonstrate comparable performance to state-ofthe- art models in emotion recognition tasks. This research outcome provides new insights and methodologies for further exploration in the field of speech signal processing.

Recently, a graph neural network has played a crucial role across various fields. In this paper, we designed a Graph Convolutional Network (GCN) to analyze population movement at the city level. It consists of four Graph Convolution (GC) layers, with each layer responsible for aggregating knowledge from its neighboring nodes and updating the feature representation for each city. We utilized population mobility data from China, which includes daily city-to-city movement data. GCN estimates the strength of relationships among all cities. Experimental results demonstrate that the proposed GCN achieves improved performance in estimating city-to-city migration flow relationships.

The rapid growth of e-commerce platforms demands recommendation systems that balance scalability with representational expressiveness. While Matrix Factorization (MF)-based collaborative filtering offers computational efficiency, it fails to capture high-order contextual relationships among items. Graph Neural Networks (GNNs) address this limitation but incur prohibitive computational costs in large-scale, real-time settings. To resolve this tradeoff, we propose a unified hybrid framework integrating MFbased candidate generation and GNN-based re-ranking within a retrieve–then–rank architecture. The MF stage efficiently generates a candidate set, while the GNN stage refines rankings via a heterogeneous graph incorporating user–item interactions, item attributes, and category structures. We further introduce an availability-aware scoring mechanism that integrates real-time stock information to enhance practical applicability. Extensive experiments on a large-scale e-commerce dataset demonstrate that the proposed framework consistently outperforms baseline methods, effectively mitigates data sparsity, and maintains computational efficiency suitable for real-world deployment.

In recent years, the anticipation of human mobility flow has significant applications in various domains ranging from urban planning to public health. This study proposes a hybrid Graph Neural Network and Long Short Term- Memory network-based model for nationwide human mobility prediction, effectively capturing inter-urban movement patterns. We validate the feasibility and effectiveness of our model using the Korean internal-city mobility dataset, which captures real-world population movement patterns across various urban regions. Our experimental results accurately predict inter-city mobility, advancing urban planning, health, and transport.

기업 부실 예측 모델은 부도와 같은 사후적 사건에 집중하여, 그 전 단계의 중요한 위험 신호인 비적정 감사의견을 예측하는 데 한계가 있었습니다. 기업 간 상호작용으로 전파되는 네트워크 리스크를 포착하지 못하는 한계가 있었다. 본 연구는 이러한 한계를 극복하고자 그래프 신경망(GNN)을 활용하여 기업의 구조적 연결성을 반영한 비적정 감사의견 예측 모델을 제안한다. 금융감독원 DART 공시 데이터를 기반으로 2,057개 상장기업의 5년간 재무 정보와 감사의견을 수집하고, 기업 간 출자 관계와 감사인 공유 관계를 통합하여 약 45만 건의 엣지로 구성된 이중 관계 네트워크(Dual-edge Network)를 구축하였다. 기업의 잠재적 부실 징후는 외부감사인의 비적정 감사의견을 기준으로 정의하였으며, GCN, GraphSAGE, GATv2 등 다양한 GNN 모델을 적용하여 로지스틱 회귀 및 XGBoost와 같은 전통적 모델과 성능을 비교 평가하였다. 실험 결과, 하이퍼파라미터 튜닝을 거친 GraphSAGE 모델이 비적정 감사의견 기업(소수 클래스) 탐지 성능에서 F1-Score 0.551과 ROC-AUC 0.936을 기록하며 종합적으로 가장 안정적인 예측력을 보였다. 이는 기업 네트워크 내 위험 신호를 효과적으로 일반화하여 학습하는 능력의 중요성을 입증한다. GNNExplainer를 통한 분석은 모델 예측의 해석 가능성을 제시했으며, 본 연구 결과는 금융기관, 투자자, 감사인이 기업의 감사 리스크(Audit Risk)를 사전에 식별하는 조기 경보 시스템으로 활용되어 실무적으로 기여할 수 있다. 향후 시계열 데이터를 반영한 동적 그래프 모델링 및 공급망 등 다양한 관계를 통합하는 연구로 확장될 수 있을 것이다.

Traditional models for predicting corporate failure or audit risk have relied heavily on the financial indicators of individual firms, failing to capture network risks that propagate through inter-firm interactions. To address this limitation, this study proposes an audit risk prediction model that incorporates structural connectivity among companies using Graph Neural Networks (GNNs). Based on public disclosure data from the Financial Supervisory Service’s DART system, we collected five years of financial information and audit opinions for 2,057 listed companies. By integrating equity investment relationships and shared auditor connections, we constructed a dual-edge network consisting of approximately 450,000 edges. Audit risk was defined according to external audit opinions, and various GNN models—including GCN, GraphSAGE, and GATv2—were evaluated and compared with traditional models such as logistic regression and XGBoost. Experimental results show that the hyperparameter-tuned GraphSAGE model achieved the most stable and comprehensive predictive performance in detecting at-risk firms (minority class), with an F1-Score of 0.551 and an ROC-AUC of 0.936. This highlights the importance of effectively learning and generalizing risk signals embedded within corporate networks. Moreover, analysis using GNNExplainer provided interpretability into the model’s predictions. The findings of this study offer practical implications for improving audit risk management systems and advancing network-based risk management in financial institutions. Future research can be extended to dynamic graph modeling by incorporating time-series data and integrating additional relational dimensions such as supply chain links.

설비의 결함 진단은 ITS분야 중 유지보수 및 안전성을 특히 더 고려해야하는 교통과는 뗄 수 없는 분야이다. 그 중, 많은 인원이 사용하는 대중교통은 그 특성상 사용하는 인원의 규모차이로 인해 유지보수 측면 뿐만 아니라 안전성을 위해서도 설비에 대한 결함 진단은 중요하다. 각 모듈들에 센서를 부착하여 전체적인 모니터링을 가능케 하는 기술은 현재 자율주행차와 전기차등의 분야에서 어느정도의 가능성을 보이고 있다. 취득되는 데이터들이 있다면 그곳에 딥러닝을 적용하는 것 또한 가능할 것이며 이러한 관점에서 여러 설비에 부착된 센서데이터들을 활용한 결함진단에 딥러닝을 적용하고자 하는 연구가 발전하고 있다. 본 연구에서는 지하철 열차 보깅을 1:2 규모로 설계된 고장 시뮬레이션 실험 플랫폼에서 수집된 다중 결함 데이터를 활용하여 기존의 많이 연구되는 단일 모듈의 결함이 아닌 다중 결함 탐지를 진행하고자 한다. Transformer와 Graph Attention을 활용한 아키텍쳐를 설계했다. Graph Attention Convolution의 다중결함 진단의 적용가능성을 탐색하엿고 그 가능성을 볼 수 있다.

본 논문은 음성신호를 순환 또는 선형그래프로 모델링하여 노드 특성 추출 및 효과적인 노드 간 관계 분석을 기반으 로 하는 음성감정인식 방법을 제안한다. 제안한 방법은 그래프 데이터를 처리하는 데 있어 각각의 강점을 활용하기 위해 그래프 합성곱 신경망(GCN)과 그래프 어텐션 신경망(GAT) 계층을 결합한다. 구체적으로, GCN을 사용하여 이웃 노드로부터 정보를 집계함으로써 노드 간의 로컬 관계를 포착한다. 또한, GAT 메커니즘을 활용하여 다양한 이웃 노드에 가중치를 부여하여 노드 간의 복잡한 글로벌 관계를 더 잘 포착할 수 있도록 한다. 실험에서는 IEMOCAP 데이터셋을 사용하여 우리의 접근 방식을 검증하고, 감정 인식 작업에서 최신 모델과 견줄 만한 성능을 보여준다. 이 연구 결과는 음성 신호 처리 분야에서 추가 탐구를 위한 새로운 통찰력과 방법론을 제공한다.

This paper proposes a speech emotion recognition method based on modeling speech signals as circular or linear graphs, enabling the extraction of node characteristics and practical analysis of relationships between nodes. The proposed method combines Graph Convolutional Networks(GCN) and Graph Attention Networks(GAT) layers to leverage the strengths of each in processing graph data. Precisely, GCN captures local relationships between nodes by aggregating information from neighboring nodes. The GAT mechanism better captures complex global relationships between nodes by assigning weights to neighboring nodes. Experiments validate our approach using the IEMOCAP dataset, demonstrating performance comparable to state-of-the-art models in emotion recognition tasks. The results of this study provide new insights and methodologies for further exploration in the field of speech signal processing.

휴지기 동안 두뇌의 기능적 네트워크 특성은 정신 병리나 인지 처리의 개인차와 관련이 있다고 알려져 있다. 본 연구는 성격 특성 중 하나로 잘 알려진 행동 접근 체계(behavioral approach system, BAS) 민감성 정도의 개인차이가 휴지기 두뇌 네트워크 특성과 같은 생물학적 특성의 차이에 근거하고 있는지를 조사하기 위해 수행되었다. 이를 위하여 정상 성인(N=30)으로부터 아무런 과제를 수행하지 않는 휴지기 동안 fMRI 영상이 획득되었고, 두뇌를 영역(node)간 서로 연결(edge)된 네트워크로 간주하고, 이를 분석하기 위해 그래프 이론 기반 네트워크 분석 방법을 적용하였다. 그 결과, 보상 처리에 관여한다고 알려진 측핵에서 네트워크를 구성하는 다른 영역들에 미치는 영향력에 대한 지표(betweenness)가 BAS 민감성이 높은 개인일수록 더 높은 경향이 있는 것으로 나타났다. 그리고 우반구 시각 피질에서 군집 계수 감소, 국소적 효율성 감소, 그리고 매개중심성의 증가와 같은 특징들이 관찰되어, 국소적 정보처리 보다 광범위한 범위의 네트워크 정보처리가 높은 경향을 보여주었다. 그 외에도 BAS 민감성이 높은 개인에서 우반구 상전두회의 연결성(degree)과 정보처리 효율성(global efficiency)이 감소된 경향이 발견되었다. 본 연구의 결과들은 측핵, 시각 피질, 전두 피질에서 두뇌 네트워크 특성의 개인차가 BAS가 높은 개인들이 보이는 보상 민감성, 자극 추구 성향, 그리고 충동성과 관련이 있음을 시사한다.

The properties of functional brain networks during rest are known to be related to individual differences in cognitive processing or psychopathology. Here, we ask if a personality trait, the behavioral approach system (BAS), is based on individual differences in neurobiological substrates, namely those of the resting-state functional brain network. Resting-state fMRI data were acquired for 30 healthy, normal participants during rest, and brain networks were analyzed using a graph-theoretical approach in which the brain is viewed as a network composed of connections (edges) between brain regions (nodes). The influence of the left nucleus accumbens on other brain regions (quantified by the metric ‘betweenness’), was found to greater tendency in those individuals with higher BAS sensitivity. High BAS sensitivity was also related to a higher tendency for global information processing in the network, rather than local information processing in the right visual cortex, as indicated by increased betweenness and decreased clustering and local efficiency. Finally, Higher BAS-sensitivity individuals also showed tendency of decreased connectivity (‘degree’) and information processing efficiency (‘global efficiency’) in the right superior frontal gyrus. These findings suggest that the differences in brain network properties in the nucleus accumbens, visual cortex, and frontal cortex are related to the greater reward sensitivity, high novelty seeking, and greater impulsivity in high-BAS individuals.

다양한 스마트 기기 및 컴퓨팅 디바이스의 보급에 따라 빅데이터 생성이 광범위하게 일어나고 있다. 기계학습 은 데이터의 패턴을 학습하여 추론을 수행하는 알고리즘이다. 다양한 기계학습 알고리즘 중에서 주목을 받는 알고리 즘은 신경망 기반의 딥러닝 학습이다. 딥러닝은 다양한 응용이 발표되면서 빠른 성능 향상을 달성하고 있다. 최근 딥 러닝 알고리즘 중에서 그래프 구조를 활용하여 데이터를 분석하려는 시도가 증가하고 있다. 본 연구에서는 그래프 구 조를 활용하여 딥러닝 네트워크에 전달하기 위한 그래프 생성 방법을 제시한다. 본 논문은 그래프 생성 과정에서 노 드의 속성과 간선의 가중치를 일반화하고 행렬화 과정을 제시하여 딥러닝 입력에 필요한 구조로 전환하는 방법을 제 시한다. 그래프 생성 과정에서 속성과 가중치 정보를 보전할 수 있는 선형변환 매트릭스 적용 방법을 제시한다. 마지 막으로 일반 그래프의 딥러닝 입력 구조를 제시하고 성능 분석을 위한 접근법을 제시한다.

With the spread of various smart devices and computing devices, big data generation is occurring widely. Machine learning is an algorithm that performs reasoning by learning data patterns. Among the various machine learning algorithms, the algorithm that attracts attention is deep learning based on neural networks. Deep learning is achieving rapid performance improvement with the release of various applications. Recently, among deep learning algorithms, attempts to analyze data using graph structures are increasing. In this study, we present a graph generation method for transferring to a deep learning network. This paper proposes a method of generalizing node properties and edge weights in the graph generation process and converting them into a structure for deep learning input by presenting a matricization We present a method of applying a linear transformation matrix that can preserve attribute and weight information in the graph generation process. Finally, we present a deep learning input structure of a general graph and present an approach for performance analysis.

Recently, various deep learning-based approaches have been studied to interpret complex human actions. A significant limitation of conventional methods is their reliance on a fixed graph structure, which restricts their ability to capture discriminative receptive fields. In this paper, we design a deformable spatio-temporal attention-based graph convolutional neural network (deformable STA-GCN) that dynamically identifies informative joints and temporal patterns. The proposed framework integrates two key modules, a deformable spatial attention (DeSA) module and a deformable temporal attention (DeTA) module. The DeSA module is integrated to dynamically identify joints closely related to motion, and the DeTA module is incorporated to identify informative temporal receptive fields. Within each module, an attention mechanism is employed to selectively emphasize the sampled features, enabling the network to extract the most discriminative spatiotemporal information. Experimental results on the NTU RGB+D benchmark dataset demonstrate that the proposed model achieves higher accuracy than the comparison model.

Traffic flow prediction is of great significance in urban planning and traffic management. As the complexity of urban traffic increases, existing prediction methods still face challenges, especially for the fusion of spatiotemporal information and the capture of long-term dependencies. This study aims to use the fusion model of graph neural network to solve the spatio-temporal information fusion problem in traffic flow prediction. We propose a new deep learning model Spatio-Temporal Information Fusion using Graph Neural Networks (STFGNN). We use GCN module, TCN module and LSTM module alternately to carry out spatiotemporal information fusion. GCN and multi-core TCN capture the temporal and spatial dependencies of traffic flow respectively, and LSTM connects multiple fusion modules to carry out spatiotemporal information fusion. In the experimental evaluation of real traffic flow data, STFGNN showed better performance than other models.

본 논문은 그래프 합성곱 신경망을 이용한 신경망 구조 탐색 모델 설계를 제안한다. 딥 러닝은 블랙박스로 학 습이 진행되는 특성으로 인해 설계한 모델이 최적화된 성능을 가지는 구조인지 검증하지 못하는 문제점이 존재한다. 신경망 구조 탐색 모델은 모델을 생성하는 순환 신경망과 생성된 네트워크인 합성곱 신경망으로 구성되어있다. 통상 의 신경망 구조 탐색 모델은 순환신경망 계열을 사용하지만 우리는 본 논문에서 순환신경망 대신 그래프 합성곱 신 경망을 사용하여 합성곱 신경망 모델을 생성하는 GC-NAS를 제안한다. 제안하는 GC-NAS는 Layer Extraction Block을 이용하여 Depth를 탐색하며 Hyper Parameter Prediction Block을 이용하여 Depth 정보를 기반으로 한 spatial, temporal 정보(hyper parameter)를 병렬적으로 탐색합니다. 따라서 Depth 정보를 반영하기 때문에 탐색 영 역이 더 넓으며 Depth 정보와 병렬적 탐색을 진행함으로 모델의 탐색 영역의 목적성이 분명하기 때문에 GC-NAS대 비 이론적 구조에 있어서 우위에 있다고 판단된다. GC-NAS는 그래프 합성곱 신경망 블록 및 그래프 생성 알고리즘 을 통하여 기존 신경망 구조 탐색 모델에서 순환 신경망이 가지는 고차원 시간 축의 문제와 공간적 탐색의 범위 문 제를 해결할 것으로 기대한다. 또한 우리는 본 논문이 제안하는 GC-NAS를 통하여 신경망 구조 탐색에 그래프 합성 곱 신경망을 적용하는 연구가 활발히 이루어질 수 있는 계기가 될 수 있기를 기대한다.

This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, a lot of efforts have been made in the field of data science to help combat against this disease. Among them, forecasting the number of cases of infection is a crucial problem to predict the development of the pandemic. Many deep learning-based models can be applied to solve this type of time series problem. In this research, we would like to take a step forward to incorporate spatial data (geography) with time series data to forecast the cases of region-level infection simultaneously. Specifically, we model a single spatio-temporal graph, in which nodes represent the geographic regions, spatial edges represent the distance between each pair of regions, and temporal edges indicate the node features through time. We evaluate this approach in COVID-19 in a Korean dataset, and we show a decrease of approximately 10% in both RMSE and MAE, and a significant boost to the training speed compared to the baseline models. Moreover, the training efficiency allows this approach to be extended for a large-scale spatio-temporal dataset.

본 논문은 챕터 수준의 강의 동영상 추천 시스템에 있어서 추천의 정확도와 처리속도 간의 균형문제, 즉, 추천 정확도를 향상시키려면 처리 속도가 저하되고, 반대로 처리 속도를 높일 경우 정확도가 감소하는 문제에 대하여 연구한다. 본 논문에서는 이의 해결을 위하여 TF-IDF, K-Means++ Clustering, Graph Neural Network(GNN) 등 다양한 기법을 복합적으로 활용하는 방법을 제안한다. 즉, 챕터들의 유사성을 바탕으로 클러스터를 사전에 구성함으로써 검색 시의 계산량을 줄여 속도를 향상시키면서도, 클러스터를 노드로 하는 그래프에 대하여 GNN을 적용함으로써 추천의 정확도를 향상시키는 방법을 제안한다. 실험 결과 GNN을 사용한 경우 추천의 정확도가 MRR 지표에서 약 19.7% 증가하였으며, 유사도 기반의 정밀도에 있어서 약 27.7% 증가하는 결과를 확인할 수 있었다. 이를 통해 학습자의 질의에 보다 적합한 동영상 챕터를 추천하는 학습시스템 구축에 기여할 것으로 기대한다.

In this paper, we propose a system for recommending lecture videos at the chapter level, addressing the balance between accuracy and processing speed in chapter-level video recommendations. Specifically, it has been observed that enhancing recommendation accuracy reduces processing speed, while increasing processing speed decreases accuracy. To mitigate this trade-off, a hybrid approach is proposed, utilizing techniques such as TF-IDF, k-means++ clustering, and Graph Neural Networks (GNN). The approach involves pre-constructing clusters based on chapter similarity to reduce computational load during recommendations, thereby improving processing speed, and applying GNN to the graph of clusters as nodes to enhance recommendation accuracy. Experimental results indicate that the use of GNN resulted in an approximate 19.7% increase in recommendation accuracy, as measured by the Mean Reciprocal Rank (MRR) metric, and an approximate 27.7% increase in precision defined by similarities. These findings are expected to contribute to the development of a learning system that recommends more suitable video chapters in response to learners' queries.